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SceneDreamer

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About

SceneDreamer is an unconditional generative model designed for creating unbounded 3D scenes, synthesizing large-scale 3D landscapes from random noises. This framework learns exclusively from in-the-wild 2D image collections without requiring any 3D annotations. Its core paradigm includes an efficient bird's-eye-view (BEV) representation, which consists of a height field for surface elevation and a semantic field for scene details. This BEV representation offers quadratic complexity for 3D scene representation, disentangled geometry and semantics, and efficient training. The tool also employs a novel generative neural hash grid to parameterize the latent space using 3D positions and scene semantics, encoding generalizable features. Finally, a neural volumetric renderer, trained adversarially on 2D images, produces photorealistic results. SceneDreamer excels in generating vivid and diverse unbounded 3D worlds.

Platform
Web
Task
scene generation

Features

produces photorealistic images via a neural volumetric renderer

employs a novel generative neural hash grid

utilizes an efficient bird's-eye-view (bev) scene representation

allows free camera trajectory

ensures 3d consistency and well-defined depth

synthesizes diverse landscapes across different styles

learns from in-the-wild 2d image collections

unbounded 3d scene generation

Pricing Plans

Free
Free Plan

Generate unbounded 3D scenes

Utilize in-the-wild 2D image collections

Efficient BEV representation

Photorealistic rendering

Code available on GitHub

Demo available on Hugging Face Spaces

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